Detection of postharvest quality loss in broccoli by means of non-colorimetric reflection spectroscopy and hyperspectral imaging

Kabakeris, Theresa; Poth, Andreas; Intreß, Joachim; Schmidt, Uwe; Geyer, Martin

doi:10.1016/j.compag.2015.09.013

Cited by 8 publications

(2 citation statements)

References 33 publications

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“…Xue and Yang [23] measured chlorophyll content in some kinds of leafy vegetables using hyperspectral camera. Kabakeriset al [24] evaluated quality loss of broccoli by hyperspectral imaging.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Makino

Kousaka

2020

Foods

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Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10−5 mg·g−1·d−1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

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Section: Introductionmentioning

confidence: 99%

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Makino

Kousaka

2020

Foods

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“…Thus, spectral information can be obtained by hyperspectral imaging technology and the composition content of samples can be analyzed by using chemometrics. In recent years, the application of hyperspectral imaging technology in quality and safety detection has been reported in many different kinds of agricultural products, such as fruits and vegetables (Dong & Guo, 2015;Guo, Zhao, & Dong, 2016;Kabakeris, Poth, Intreß, Schmidt, & Geyer, 2015;Rady, Guyer, & Lu, 2015), grains (Fei et al, 2018;Huang, Wang, Zhang, & Zhu, 2014;Wang et al, 2015), meat products (Garrido-Novell, Garrido-Varo, Pérez-Marín, & Guerrero, 2017;Kamruzzaman, Makino, Oshita, & Liu, 2015;Zhao, Esquerre, Downey, & O'Donnell, 2017), and so on.…”

Section: Introductionmentioning

confidence: 99%

Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Sun

Shi

Zhang

et al. 2019

J Food Process Engineering

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Hyperspectral imaging technology at 416–1000 nm was investigated to detect moisture content in peanut kernels. Four varieties of peanuts were scanned using a “push‐broom” system to acquire hyperspectral images. In this study, three models including partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVR) were established to detect moisture content in peanut kernels based on full wavelengths. The performance of SVR was the best with determination coefficient (R2) of .9432, root mean square errors (RMSE) of 0.7054%, and residual prediction deviation (RPD) of 3.9694 for prediction set. In order to simplify modeling process and improve calculation speed of the models, successive projections algorithm (SPA) and regression coefficient were applied for optimal wavelengths selection. Then, PCR, PLSR, and SVR models were established based on these selected wavelengths, respectively. As a result, SPA–SVR generated a satisfied effect with R2 of .9363, RMSE of 0.7021%, and RPD of 3.988 for prediction set. All results in this study indicated that the combination of chemometrics and hyperspectral imaging technology could achieve rapid and nondestructive detection of moisture content in peanut kernels. Practical applications The quality of peanut has a direct relationship with the moisture content in peanut. If moisture content in peanut is over standard, the peanut storage time will be shorter and also easy to be bad. It is harmful to eat this kind of peanut. Traditional methods for detection of peanut moisture content are tedious, time‐consuming, and even greatly influenced by subjective factors. As an emerging technique, hyperspectral imaging technology can detect moisture content in peanut kernels rapidly, accurately, and nondestructive. In this study, we generated a best prediction model, successive projections algorithm–support vector machine regression, which was established based on several characteristic wavelengths. All these results provided a theoretical basis for the detection of portable moisture content in peanut kernels.

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Freshness Assessment of Broccoli using Electronic Nose

Ezhilan¹,

Nesakumar

Babu³

et al. 2019

Measurement

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Detection of postharvest quality loss in broccoli by means of non-colorimetric reflection spectroscopy and hyperspectral imaging

Cited by 8 publications

References 33 publications

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Detection of moisture content in peanut kernels using hyperspectral imaging technology coupled with chemometrics

Freshness Assessment of Broccoli using Electronic Nose

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